Talks # 10: Tanishq Abraham; What are CycleGANs? (a novel deep learning tool in pathology)
Abstract: A CycleGAN is a variant of the generative adversarial network (GAN) architecture designed to handle unpaired image conversion problems. In this talk, Tanishq will describe what are CycleGANs, what applications they are best suited for, and their pitfalls. Additionally, he will walk through code for model training and inference as part of a code demo. Finally, he will present his own research applying CycleGANs (and related models) to pathology and microscopy.
Bio: Tanishq Abraham is considered a child genius and a prodigy. He graduated high school at 10 years old with a 4.0 GPA and …
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Intro
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Episode 1.1: Intro and building a machine learning framework
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Episode 1.2: Building an inference for the machine learning framework
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Tips N Tricks #1: Send messages to Slack using Python
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Episode 2: A Cross Validation Framework
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Tips N Tricks #2: Setting up development environment for machine learning
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Episode 3: Handling Categorical Features in Machine Learning Problems
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BERT on Steroids: Fine-tuning BERT for a dataset using PyTorch and Google Cloud TPUs
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Tips N Tricks #3: Creating a clean inference kernel/notebook on Kaggle
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Special Announcement: Approaching (almost) any machine learning problem
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Training BERT Language Model From Scratch On TPUs
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Bengali.AI: Handwritten Grapheme Classification Using PyTorch (Part-1)
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Bengali.AI: Handwritten Grapheme Classification Using PyTorch (Part-2)
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Episode 4: Simple and Basic Binary Classification Metrics
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Training Sentiment Model Using BERT and Serving it with Flask API
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Tips N Tricks #4: Using joblib to speed up almost any function (example 1)
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Episode 5: Entity Embeddings for Categorical Variables
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Tips N Tricks #5: 3 Simple and Easy Ways to Cache Functions in Python
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Multi-Lingual Toxic Comment Classification using BERT and TPUs with PyTorch
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Data Processing For Question & Answering Systems: BERT vs. RoBERTa
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Tips N Tricks #6: How to train multiple deep neural networks on TPUs simultaneously
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Talks # 1:Andrey Lukyanenko - Handwritten digit recognition w/ a twist & topic modelling over time
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Episode 6: Simple and Basic Evaluation Metrics For Regression
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Talks # 2: Subhaditya Mukherjee - Image restoration using Deep Learning: Dehazing
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Basic git commands everyone should know about
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How do I start my career in Data Science?
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Talks # 3: Lorenzo Ampil - Introduction to T5 for Sentiment Span Extraction
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Tips & Tricks # 7: Fast, convenient & awesome code formatting using Black
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Talks # 4: Sebastien Fischman - Pytorch-TabNet: Beating XGBoost on Tabular Data Using Deep Learning
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Build a web-app to serve a deep learning model for skin cancer detection
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Talks # 5: Parul Pandey: Data Science, Diversity and Kaggle
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Implementing original U-Net from scratch using PyTorch
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Tips N Tricks # 8: Using automatic mixed precision training with PyTorch 1.6
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Talks # 6: Mani Sarkar: From backend development to machine learning
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Dockerizing the skin cancer detection web application
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How to train a deep learning model using docker?
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Building an entity extraction model using BERT
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Talks # 7: Moez Ali: Machine learning with PyCaret
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How to convert almost any PyTorch model to ONNX and serve it using flask
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Hyperparameter Optimization: This Tutorial Is All You Need
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I finally got a copy of "Approaching (Almost) Any Machine Learning Problem"
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Live Q&A: Getting Started With Data Science
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WTFML: Simple, reusable code for PyTorch models
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Talks # 8: Sebastián Ramírez; Build a machine learning API from scratch with FastAPI
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BERT Model Architectures For Semantic Similarity
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Pair Programming: Deep Learning Model For Drug Classification With Andrey Lukyanenko
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